from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 33.0 | 12.007868 |
| daal4py_KNeighborsClassifier | 0.0 | 6.0 | 22.106278 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 37.627022 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 31.648181 |
| KMeans_tall | 0.0 | 0.0 | 26.798776 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 10.952872 |
| KMeans_short | 0.0 | 0.0 | 3.723011 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.964744 |
| LogisticRegression | 0.0 | 0.0 | 26.822800 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 5.988052 |
| Ridge | 0.0 | 0.0 | 11.874550 |
| daal4py_Ridge | 0.0 | 0.0 | 2.690213 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 0.593164 |
| lightgbm | 0.0 | 5.0 | 18.704248 |
| xgboost | 0.0 | 5.0 | 16.497829 |
| catboost | 0.0 | 5.0 | 41.355511 |
| total | 1.0 | 5.0 | 31.466991 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.595 | 0.000 | 1.345 | 0.000 | 1 | 100 | NaN | NaN | 0.568 | 0.000 | 1.047 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.949 | 0.325 | 0.000 | 0.025 | 1 | 100 | 0.927 | 0.954 | 4.561 | 0.068 | 5.471 | 0.108 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.228 | 0.011 | 0.000 | 0.228 | 1 | 100 | 1.000 | 1.000 | 0.108 | 0.007 | 2.118 | 0.168 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.137 | 0.000 | 5.828 | 0.000 | -1 | 1 | NaN | NaN | 0.553 | 0.000 | 0.248 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 31.497 | 0.000 | 0.000 | 0.031 | -1 | 1 | 0.711 | 0.954 | 4.582 | 0.042 | 6.874 | 0.063 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.204 | 0.025 | 0.000 | 0.204 | -1 | 1 | 1.000 | 1.000 | 0.108 | 0.002 | 1.891 | 0.233 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.765 | 0.000 | -1 | 5 | NaN | NaN | 0.567 | 0.000 | 0.245 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 38.166 | 0.000 | 0.000 | 0.038 | -1 | 5 | 0.797 | 0.818 | 4.484 | 0.035 | 8.512 | 0.066 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.017 | 0.000 | 0.201 | -1 | 5 | 1.000 | 1.000 | 0.104 | 0.002 | 1.944 | 0.163 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.145 | 0.000 | 5.534 | 0.000 | 1 | 1 | NaN | NaN | 0.546 | 0.000 | 0.265 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 18.141 | 0.163 | 0.000 | 0.018 | 1 | 1 | 0.711 | 0.719 | 4.480 | 0.024 | 4.049 | 0.043 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.224 | 0.007 | 0.000 | 0.224 | 1 | 1 | 1.000 | 1.000 | 0.106 | 0.004 | 2.109 | 0.102 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.141 | 0.000 | 5.659 | 0.000 | -1 | 100 | NaN | NaN | 0.560 | 0.000 | 0.252 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 38.783 | 0.000 | 0.000 | 0.039 | -1 | 100 | 0.927 | 0.818 | 4.504 | 0.030 | 8.610 | 0.057 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.025 | 0.000 | 0.201 | -1 | 100 | 1.000 | 1.000 | 0.107 | 0.002 | 1.880 | 0.238 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.138 | 0.000 | 5.790 | 0.000 | 1 | 5 | NaN | NaN | 0.544 | 0.000 | 0.254 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.809 | 0.347 | 0.000 | 0.025 | 1 | 5 | 0.797 | 0.719 | 4.530 | 0.048 | 5.476 | 0.096 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.222 | 0.007 | 0.000 | 0.222 | 1 | 5 | 1.000 | 1.000 | 0.112 | 0.006 | 1.991 | 0.129 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.280 | 0.000 | 1 | 100 | NaN | NaN | 0.106 | 0.000 | 0.542 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.649 | 0.171 | 0.000 | 0.021 | 1 | 100 | 0.987 | 0.986 | 1.083 | 0.017 | 19.059 | 0.338 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.002 | 0.000 | 0.025 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 5.413 | 0.591 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.286 | 0.000 | -1 | 1 | NaN | NaN | 0.098 | 0.000 | 0.567 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 25.027 | 0.246 | 0.000 | 0.025 | -1 | 1 | 0.977 | 0.986 | 1.083 | 0.026 | 23.103 | 0.595 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 4.507 | 0.544 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.304 | 0.000 | -1 | 5 | NaN | NaN | 0.098 | 0.000 | 0.538 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.334 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.986 | 0.985 | 1.008 | 0.013 | 33.074 | 0.442 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.002 | 0.000 | 0.030 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 6.451 | 0.634 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.284 | 0.000 | 1 | 1 | NaN | NaN | 0.102 | 0.000 | 0.553 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 11.695 | 0.069 | 0.000 | 0.012 | 1 | 1 | 0.977 | 0.978 | 1.006 | 0.020 | 11.621 | 0.237 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.014 | 0.002 | 0.000 | 0.014 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.832 | 0.505 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.306 | 0.000 | -1 | 100 | NaN | NaN | 0.096 | 0.000 | 0.546 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 33.788 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.987 | 0.985 | 1.022 | 0.035 | 33.061 | 1.143 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.002 | 0.000 | 0.030 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 6.619 | 0.699 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.049 | 0.000 | 0.324 | 0.000 | 1 | 5 | NaN | NaN | 0.103 | 0.000 | 0.481 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.312 | 0.094 | 0.000 | 0.020 | 1 | 5 | 0.986 | 0.978 | 1.001 | 0.011 | 20.300 | 0.244 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.025 | 0.001 | 0.000 | 0.025 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.209 | 0.550 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.768 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.778 | 0.000 | 3.556 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.744 | 0.030 | 0.000 | 0.001 | 1 | 1 | 0.957 | 0.977 | 0.210 | 0.005 | 3.533 | 0.167 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 3.080 | 1.736 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.766 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.778 | 0.000 | 3.558 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.757 | 0.070 | 0.000 | 0.005 | 1 | 100 | 0.976 | 0.961 | 0.117 | 0.005 | 40.741 | 1.840 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 11.723 | 7.011 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.928 | 0.000 | 0.027 | 0.000 | -1 | 1 | NaN | NaN | 0.755 | 0.000 | 3.876 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.414 | 0.006 | 0.000 | 0.000 | -1 | 1 | 0.957 | 0.973 | 0.648 | 0.014 | 0.639 | 0.017 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 4.795 | 1.788 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.774 | 0.000 | 0.029 | 0.000 | 1 | 5 | NaN | NaN | 0.770 | 0.000 | 3.603 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.405 | 0.036 | 0.000 | 0.001 | 1 | 5 | 0.973 | 0.961 | 0.116 | 0.008 | 12.072 | 0.913 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 3.732 | 1.926 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.769 | 0.000 | 0.029 | 0.000 | -1 | 100 | NaN | NaN | 0.751 | 0.000 | 3.688 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.801 | 0.063 | 0.000 | 0.003 | -1 | 100 | 0.976 | 0.977 | 0.209 | 0.007 | 13.397 | 0.547 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.001 | 0.000 | 0.006 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 16.909 | 8.013 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.709 | 0.000 | 0.030 | 0.000 | -1 | 5 | NaN | NaN | 0.738 | 0.000 | 3.672 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.851 | 0.021 | 0.000 | 0.001 | -1 | 5 | 0.973 | 0.973 | 0.646 | 0.015 | 1.317 | 0.044 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 6.981 | 3.877 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.721 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.523 | 0.000 | 1.379 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.029 | 0.002 | 0.001 | 0.000 | 1 | 1 | 0.982 | 0.981 | 0.001 | 0.000 | 21.556 | 5.554 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.563 | 3.800 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.736 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.525 | 0.000 | 1.403 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.058 | 0.005 | 0.000 | 0.000 | 1 | 100 | 0.984 | 0.975 | 0.001 | 0.000 | 63.016 | 22.917 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.473 | 3.081 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.721 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.513 | 0.000 | 1.407 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.032 | 0.001 | 0.000 | 0.000 | -1 | 1 | 0.982 | 0.979 | 0.009 | 0.001 | 3.603 | 0.501 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 13.895 | 7.881 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.756 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.539 | 0.000 | 1.402 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.032 | 0.002 | 0.001 | 0.000 | 1 | 5 | 0.986 | 0.975 | 0.001 | 0.000 | 36.009 | 11.224 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.700 | 2.599 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.724 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.526 | 0.000 | 1.376 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.052 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.984 | 0.981 | 0.001 | 0.000 | 40.593 | 9.814 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 12.321 | 7.080 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.734 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.536 | 0.000 | 1.368 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.038 | 0.008 | 0.000 | 0.000 | -1 | 5 | 0.986 | 0.979 | 0.008 | 0.001 | 4.812 | 1.096 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 14.454 | 7.400 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.745 | 0.000 | 0.644 | 0.000 | k-means++ | NaN | 30 | NaN | 0.320 | 0.0 | 2.328 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.281 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 5.837 | 5.294 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.552 | 5.018 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.633 | 0.000 | 0.758 | 0.000 | random | NaN | 30 | NaN | 0.337 | 0.0 | 1.876 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.276 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 6.919 | 3.544 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.046 | 3.296 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 8.619 | 0.000 | 2.784 | 0.000 | k-means++ | NaN | 30 | NaN | 4.107 | 0.0 | 2.099 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 11.979 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.115 | 2.746 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.001 | 0.012 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.521 | 6.338 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.652 | 0.000 | 3.136 | 0.000 | random | NaN | 30 | NaN | 4.312 | 0.0 | 1.775 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.639 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.200 | 2.499 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.014 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.909 | 4.552 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.327 | 0.0 | 0.010 | 0.000 | k-means++ | NaN | 20 | NaN | 0.157 | 0.0 | 2.079 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.146 | 0.000 | k-means++ | 0.001 | 20 | 0.005 | 0.001 | 0.0 | 2.919 | 0.669 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.612 | 3.396 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.109 | 0.0 | 0.029 | 0.000 | random | NaN | 20 | NaN | 0.060 | 0.0 | 1.824 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.143 | 0.000 | random | 0.002 | 20 | 0.001 | 0.001 | 0.0 | 3.026 | 0.717 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.232 | 3.143 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.190 | 0.0 | 0.134 | 0.000 | k-means++ | NaN | 20 | NaN | 0.665 | 0.0 | 1.790 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 4.624 | 0.000 | k-means++ | 0.332 | 20 | 0.364 | 0.002 | 0.0 | 2.128 | 0.311 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.855 | 4.040 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.387 | 0.0 | 0.414 | 0.000 | random | NaN | 20 | NaN | 0.275 | 0.0 | 1.406 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 4.759 | 0.000 | random | 0.290 | 20 | 0.308 | 0.002 | 0.0 | 2.101 | 0.231 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.256 | 4.587 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 16.194 | 0.0 | [-0.07285741] | 0.000 | NaN | NaN | NaN | NaN | NaN | 3.139 | 0.0 | 5.159 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [36.51824965] | 0.000 | NaN | NaN | NaN | NaN | 0.496 | 0.000 | 0.0 | 0.895 | 0.259 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.1426161] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.384 | 0.297 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 1.433 | 0.0 | [1.45180845] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.160 | 0.0 | 1.235 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.003 | 0.0 | [82.69971963] | 0.000 | NaN | NaN | NaN | NaN | 0.280 | 0.004 | 0.0 | 0.622 | 0.091 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [11.80015976] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.180 | 0.132 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.330 | 0.000 | 0.242 | 0.0 | NaN | NaN | NaN | 0.342 | 0.000 | 0.965 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.486 | 0.0 | NaN | NaN | 0.097 | 0.022 | 0.001 | 0.559 | 0.045 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.759 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.573 | 0.323 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.705 | 0.000 | 0.469 | 0.0 | NaN | NaN | NaN | 0.430 | 0.000 | 3.969 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 3.285 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.779 | 0.494 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.007 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.737 | 0.523 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
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"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
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"version": null,
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}
],
"cpu_count": 2
}